Achieving on-line optimization of complex, large scale chemical and petrochemical plants, is difficult. Processes associated with hydrocracking, catalytic cracking, or reforming as conventionally used in petroleum refining operations are characterized by large scale, multiple plant inputs and outputs. Moreover, the interrelationship of such multiple plant variables is largely unknown. Hence, process modeling for optimization of one or more plant inputs using internal set points, is difficult to achieve since optimization requires a high degree of modeling fidelity for plant variables. A typical example of off-line optimization by high fidelity modeling is set forth in D. M. Prett et al., U.S. Pat. No. 4,349,869 for "Dynamic Matrix Control Method" wherein optimal set points for the control system of the plant, are determined off-line. The set points are then implemented by a dynamic controller employing integral feedback and feedforward action based on output deviations from these set points.
(References presenting good overviews, include Kwakernaak, H., Sivan, R., Linear Optimal Control Systems, John Wiley & Sons, Inc., New York, 1972; Marchetti, J. L.; Mellichamp, D. H.; Seborg, D. E., "Predictive Control Based on Discrete Convolution Models", Ind. Eng. Chem. Process Des. Dev. 22, 488-495 (1983); and Athans, M. and Falb, P. L., Optimal Control: Introduction to Theory and Application, McGraw Hill (1966).)
A simple, but yet quite effective, on-line optimization approach, is a control method referred to as a constraint control (or "hill climbing"). The strategy in choosing optimal directions is usually process dependent, and may involve process goals related to maximization of certain plant variables such as maximizing flow of a certain product fraction, or it may be related to minimization goals such as minimizing energy input to the plant. On-line measurements of plant outputs (controlled variables), are of course necessary in order to determine when constraints have been reached such that further maximization/minimization must be stopped. Also the regulation and optimization efforts usually must be prioritized, so that regulatory effort is paramount, see J. P. Kennedy et al., U.S. Pat. No. 4,228,509 for "Multivariable Control System For Regulating Process Conditions and Process Optimization".
But constraint control, by definition, disturbs the plant. Manipulation of the plant inputs (in optimal directions) automatically influences other plant variables and the magnitude of the effects is hard to predict. Hence, constraint control has hitherto been exercised in a slow, non-dynamic, steady-state fashion. I.e., optimizing action is only taken in small steps with long waiting periods between each step so that a steady-state condition is reached before a new optimizing move or step is attempted. Consequently, the process spends significant time periods away from true optimal conditions and, moreover, even if the process reaches the desired optimal condition or combination of conditions, new external disturbances may occur which cause the process to move away from optimum. An example of such disturbances is a change in feed and/or recycle rates, requiring the control sequence to begin anew.
Therefore, an object of the present invention is the provision of an on-line constraint dependent control method and system in which dynamic stabilization of plant variables is integrated with the dynamic pursuit of optimization of one or more plant inputs in the time domain without the use of optimal pre-determined set points, and in which one or more plant inputs can be constantly driven in pursuit of optimality while other plant inputs reserved for stabilization maintain the plant outputs within preselected limits. Such stability is maintain even in the face of new disturbances within the plant such as changes in feed or recycle rates. In one aspect, the limiting constraint(s) need not be constant as a function of time but can be shifted from one process property to another. In another aspect, the dynamic optimizing action of the present invention also has the capability of reversing itself when a constraint becomes violated.
In this regard, the term "stabilizing control" refers to the stability enhancing effects in two ways. The first involves mitigation against the influences of disturbances. Such disturbances may be intentional; the optimization action itself is an example of the latter; they may also be unintentional as caused by true external disturbances, such as changes in feed or recycle rates. The second function concerns the interrelationship between future stabilization efforts (as, say, required to bring about stable plant operations), and the next-in-time optimization effort. Certain characteristics of the optimizing effort, as explained below, can be used to determine if optimization should occur at the next-in-time operating cycle, and the degree of control (strength) it should exert on plant operations. In other words, the optimization effort is permitted to prejudge its impact before implementation occurs, based at least in part, on the strength of the stabilization effort required to maintain stability of the plant. Thus, the present invention can avoid the situation where the optimization effort might take an action too strong for efficient stabilizing action, but still allow optimization when the plant is stable. This feature makes the present invention "artificially intelligent" because the consequences of plant actions are forecasted before actual implementation occurs.